The Influence of Social Embeddedness on Pro-Environmental Behavior of Community Residents in Giant Panda National Park

Abstract

This study is based on the theory of social embedding and selects the entrance community of Baoping County in the Giant Panda National Park as a case to explore the five core dimensions of cognitive embedding, relational embedding, structural embedding, institutional embedding, and cultural embedding and their mechanisms of influence on pro-environmental behaviors (mainly the five dimensions discussed). The study constructs a “stimulus—organism—response” (S-O-R) model, introduces two mediating variables, tourism impact perception and place attachment, and conducts empirical analysis based on 326 valid questionnaire responses using structural equation modeling. The results show that all dimensions of social embedding have a significant positive impact on pro-environmental behaviors, but the mechanisms and transmission paths vary. Each dimension indirectly influences pro-environmental behaviors through the chain path of “tourism impact perception → place attachment.” The study reveals the key role of social relationship networks, tourism perception, and emotional belonging in stimulating environmental behaviors and emphasizes the need to systemically enhance residents’ ecological responsibility awareness and action willingness through community co-construction, optimization of tourism benefits, and cultural atmosphere creation.

1. Introduction

Most of China’s national parks are located in geologically complex, underdeveloped areas inhabited by ethnic minorities [1], where local residents’ livelihood activities are deeply embedded in the natural resource systems, forming a unique pattern of integration between ecology, production, and daily life [2]. As both users and stewards of natural resources, residents’ pro-environmental behavior plays a decisive role in ecological conservation [3]. Pro-environmental behavior refers to individuals’ daily actions that actively reduce ecological negative impacts [4], characterized by both altruism and collective action [5]. Existing studies have confirmed that cognitive or affective factors such as livelihood capital [6], subjective norms [7], everyday environmental habits [8], environmental values [9], awareness of environmental consequences [10], environmental emotions [11], and place attachment [12] influence residents’ pro-environmental behavior. These studies have laid an important foundation for understanding such behavior, revealing its diverse and complex drivers.

The Ecological Redline, a strictly enforced boundary established by China to safeguard ecological security, has profoundly reshaped the relationship between humans and nature by restricting human activities in critical ecological functional zones. This policy reinforces the principle of conservation prioritization and imposes stronger ecological constraints [13]. However, as the Ecological Redline policy is implemented, tensions between conservation requirements and the development needs of local communities have become increasingly pronounced, leading to a restructuring of traditional human- environment relationships [14]. As “economic agents,” local residents may prioritize maximizing personal benefits at the expense of environmental protection, while as “social beings,” their place attachment reinforces ecological responsibility. Under the growing tension between policy and livelihood, these dual attributes intertwine to form a unique mechanism driving pro-environmental behavior [15,16].

To analyze this complex mechanism, this study employs social embeddedness theory as its core theoretical framework. This theory emphasizes that individual behavior does not occur in isolation but is shaped by relational networks and environmental contexts [17], helping individuals create opportunities, enhance capabilities, and stimulate motivation [18].

Therefore, this study adopts social embeddedness theory and takes Baoxing County in Giant Panda National Park as the case site. Grounded in the Stimulus-Organism-Response (S-O-R) framework (Mehrabian & Russell, 1974) [19], it develops and tests an integrated theoretical model. This model examines how various dimensions of social embeddedness act as an external “Stimulus,” shaping residents’ internal “Organism” states—operationalized through the key mediating variables of “perceptions of tourism impacts” and “place attachment”—which in turn trigger pro-environmental behavior as the ultimate “Response.”

2. Literature Review and Theoretical Foundation

2.1. Residents’ Pro-Environmental Behavior

Residents’ pro-environmental behavior refers to the active, practical actions taken by residents in specific regional development contexts to support national park construction and participate in ecological conservation, based on their subjective cognition of the human–nature relationship and ecological changes within their living space [20]. Essentially, it is an extension of prosocial and environmentally friendly behavior contextualized in a specific locality, with goals including improving environmental quality, protecting ecological resources, and promoting harmonious coexistence between humans and nature [21]. Existing studies indicate that such behavior is usually influenced by cognitive or affective factors. Cognitive environmental factors include perceived environmental benefits [22] and perceived environmental costs [23], while affective factors encompass social relationships [24], destination image [25], and place attachment [26]. However, current research predominantly focuses on individual rationality and emotional drivers from a psychological perspective, neglecting the social contextual attributes of behavior.

As direct recipients of this major ecological institutional reform—the establishment of national parks—community residents are not only participants in development but also subjects being profoundly reshaped. They bear the intense changes and adaptive pressures across ecological, economic, and cultural dimensions brought about by conservation policy implementation in their surrounding areas [27]. Given the complex social relationship networks within national parks, there is an urgent need for a systematic deconstruction of the factors influencing pro-environmental behavior, moving beyond single-dimensional analytical frameworks. Integrating the perspective of social embeddedness is crucial to reveal the social interaction mechanisms underpinning the formation of such behavior.

2.2. Social Embeddedness Theory

Neoclassical economics, based on the assumption of the “rational economic man,” posits that individual behavior is aimed at maximizing self-interest, independent of social relationships and outside social contexts. In contrast, sociology emphasizes the “social man” attribute, arguing that behavior is constrained by social structures and norms [28]. Social embeddedness theory breaks through this binary opposition by proposing the concept of “appropriate socialization,” stressing that individual behavior is neither an atomized decision detached from social contexts nor fully internalized by social norms but is continuously embedded in specific social networks and influenced by their structures [29].

This theory originates from Karl Polanyi’s concept of embeddedness, which asserts that economic activities are embedded within both economic and non-economic institutions [30]. Granovetter further micro-founded and operationalized this macro perspective by developing the notions of relational embeddedness (intensity of interpersonal interactions) and structural embeddedness (effects of network positions) [17]. Zukin expanded the framework by adding cognitive, cultural, and institutional embeddedness dimensions, forming a multi-level and comprehensive analytical framework [31]. Based on social embeddedness theory, this study integrates the classical framework with extended dimensions to construct a system of influencing factors for residents’ pro-environmental behavior (

Table 1. Factors Influencing Residents’ Pro-Environmental Behavior in Social Embeddedness.

Category	Connotation	Influencing Factors	References
Cognitive Embeddedness	Residents’ understanding of and attention to environmental issues	Sense of environmental responsibility Self-efficacy	[25,32,33]
Relational Embeddedness	Quality of trust, reciprocity, and emotional bonds among community residents	Strength of relationships Relationship quality	[34,35,36]
Structural Embeddedness	Residents’ positions and roles within the community network	Network density Network position	[35,36,37]
Institutional Embeddedness	Guidance and constraints on pro-environmental behavior from policies and regulations	Policies and institutions Behavioral norms	[35,36,38]
Cultural Embeddedness	Influence of community cultural facilities, supply of environmental materials, shared values, and collective conventions on residents’ pro-environmental behavior	Material culture Behavioral culture	[39,40,41]

).

Residents’ understanding of and attention to environmental issues are reflected in their sense of environmental responsibility and self-efficacy [32]. Studies have shown that the intensity of individual cognition positively drives the practice of pro-environmental behavior [42,43], essentially serving as the endogenous motivation for transforming environmental awareness into action [44]. Research on different groups, including tourists [45] and residents [9] in national parks, has confirmed that these subjective cognitive factors significantly influence behavior.

Trust, reciprocity, and emotional bonds among residents constitute the core of relational embeddedness. Focusing on social networks formed through individual interactions, both the strength of relationships (interaction frequency) and relationship quality (degree of trust) directly drive behavioral transmission [45]. Empirical evidence shows that in communities with high levels of trust, pro-environmental behavior spreads more efficiently [32].

Structural embeddedness refers to actors’ positions and roles within the network, emphasizing the dual effects of network density and centrality. The closer the community connections, the stronger the group norms’ influence on behavior; the more central an individual’s position in the network, the more pronounced the role model effect and the stronger the herd mentality [46].

Institutional embeddedness addresses the conflict between altruism and self-interest through policy incentives and normative constraints [47]. Empirical studies in protected areas have demonstrated that regulations are direct factors shaping residents’ pro-environmental behavior [48]. Government policy implementation has been identified as a key driver of behavioral transformation [49], particularly playing a foundational role in shaping awareness [50].

As the basic unit of social culture, the community serves both communicative and normative functions [39]. Mead pointed out that individual behavior is rooted in the cultural environment, and the collective cultural state determines the direction of behavioral practices [51]. Material culture (supply of facilities) and behavioral culture (shared values) together constitute a constraining mechanism [52]. The quality of public community spaces and the presence of normative guidelines significantly enhance residents’ participation in environmental protection [40]. The Social Identity Model of Pro-Environmental Action (SIMPEA) reveals that in-group norms drive behavior through collective efficacy [53], forming a transformation chain of “awareness–willingness–behavior” [54].

The communities of the Giant Panda National Park represent a combination of social relationships based on kinship, marriage ties, geographic proximity, and economic interests. Local residents share a common physical living space, ecological interaction space, emotional cohesion space, and developmental aspiration space. Cultural atmosphere, interpersonal connections, social systems, and institutional rules are interwoven, collectively shaping residents’ cognitive structures and behavioral patterns [55]. This study integrates the social embedding analysis framework constructed by Granovetter and Zukin, combining the actual situation of the Giant Panda National Park, to divide the social embedding influencing local residents’ pro-environmental behaviors into five independent but interrelated dimensions: cognitive, structural, relational, institutional, and cultural embeddedness.

2.3. The “Stimulus–Organism–Response” (S-O-R) Model

The research framework based on the S-O-R theory model offers significant advantages in analyzing individual behavioral decision-making. Its “stimulus–organism–response” logical chain systematically reveals the interactive mechanism between the external environment and internal psychology [56]. Residents’ pro-environmental behavior in national park communities is also formed through their evaluation and analysis of external stimuli, resulting in actions related to nature conservation.

This study constructs a path model in which cognitive, structural, relational, institutional, and cultural embeddedness in the Giant Panda National Park serves as the stimulus variable (S), perceptions of tourism impacts and place attachment act as the mediating organism (O), and pro-environmental behavior is the response variable (R). Through structural equation modeling, the study empirically analyzes the transmission effects cognitive, relational, structural, institutional, and cultural embeddedness on residents’ behavior, uncovering how community network embeddedness drives environmental behavior practice through psychological perceptions and emotional bonds. This provides theoretical support at the mechanistic level for optimizing community governance in national parks.

3. Research Hypotheses and Model Construction

3.1. Social Embeddedness and Residents’ Pro-Environmental Behavior

From the perspective of environmental psychology, residents’ pro-environmental behavior refers to practical actions aimed at minimizing the negative ecological impacts of their production and living activities [31]. Studies have found that such behavior is influenced not only by rational perceptions based on the “rational man” concept, such as perceived environmental benefits [57] and perceived environmental costs [58], but also by interpersonal relationship factors embedded in social structures and networks, such as place attachment [59] and relationship quality [25], which reflect the “social man” aspect. This reveals that residents’ behavioral decisions combine economic rationality and social sensitivity: they pursue optimal cost–benefit outcomes while being structurally constrained by institutional norms, cultural atmosphere, and relationship networks. Based on this analysis, the following hypotheses are proposed:

Hypothesis 1a (H1a).

COE has a positive effect on PEB.

Hypothesis 1b (H1b).

RE has a positive effect on PEB.

Hypothesis 1c (H1c).

SE has a positive effect on PEB.

Hypothesis 1d (H1d).

IE has a positive effect on PEB.

Hypothesis 1e (H1e).

CUE has a positive effect on PEB.

3.2. Mediating Role of Perceptions of Tourism Impacts

As key stakeholders in tourism destinations, local residents participate deeply throughout the entire tourism development process. Their perceptions of tourism impacts reflect their overall evaluation of the economic, environmental, and sociocultural effects induced by tourism development [60]. Residents with higher levels of social embeddedness typically possess more resources and support within their social networks [34], making them more likely to understand and assess the impacts of tourism activities from multiple perspectives [61]. Cognitive embeddedness enhances environmental sensitivity; relational embeddedness expands channels for obtaining information; structural embeddedness improves the effectiveness of group interactions; institutional embeddedness clarifies rights and responsibilities; and cultural embeddedness deepens value judgments. This multidimensional embeddedness enables residents to systematically analyze the complex impacts of tourism activities, resulting in differentiated perception patterns. Based on this analysis, the following hypotheses are proposed:

Hypothesis 2a (H1a).

COE has a positive effect on PTI.

Hypothesis 2b (H1b).

RE has a positive effect on PTI.

Hypothesis 2c (H1c).

SE has a positive effect on PTI.

Hypothesis 2d (H1d).

IE has a positive effect on PTI.

Hypothesis 2e (H1e).

CUE has a positive effect on PTI.

Research has shown that residents’ perceptions of tourism impacts significantly drive their pro-environmental behavior: when residents perceive a positive link between tourism development and ecological conservation, their willingness to engage in environmental protection and the intensity of their practices increase significantly [62]. Empirical evidence from various ecotourism and nature conservation sites, such as Qingchengshan–Dujiangyan [8] and Taibai Mountain [63], demonstrates that local residents recognize tourism development as highly dependent on ecological tourism resources, and that stronger positive perceptions of tourism impacts lead to greater acceptance of tourism and more proactive attitudes toward ecological protection. Based on this analysis, the following hypotheses are proposed:

Hypothesis 3 (H3).

PTI has a significant positive effect on PEB.

Hypothesis 4 (H4).

PTIs mediate the relationship between COE, RE, SE, IE, CUE and PEB.

3.3. Mediating Role of Place Attachment

From the perspective of human geography, place attachment, as a multidimensional bond in human—place interactions, encompasses two dimensions: functional dependence (place dependence) and emotional identification (place identity) [64,65]. Among residents of the Giant Panda National Park, place attachment is manifested as a compound emotional connection of physical space dependence and a sense of spiritual belonging [66]. The strength of this attachment is systematically shaped by the social embeddedness mechanism: cognitive embeddedness strengthens ecological stewardship awareness through a sense of environmental responsibility; relational and structural embeddedness promote community interaction networks and consolidate collective memory [67]. Institutional embeddedness constructs a policy-guided framework that coordinates the dynamic balance between conservation and development [68]. The stronger residents’ identification with community culture, the more easily they internalize shared community values, thereby enhancing their emotional bonds with the place [69]. Based on this analysis, the following hypotheses are proposed:

Hypothesis 5a (H5a).

COE has a positive effect on PA.

Hypothesis 5b (H5b).

RE has a positive effect on PA.

Hypothesis 5c (H5c).

SE has a positive effect on PA.

Hypothesis 5d (H5d).

IE has a positive effect on PA.

Hypothesis 5e (H5e).

CUE has a positive effect on PA.

Research has shown that place attachment profoundly influences individuals’ attitudes toward resource management and their behavioral practices: the stronger the emotional connection to the place, the more frequently residents engage in pro-environmental behaviors in their daily activities [26,70]. This mechanism is universal among various groups, including residents and tourists in tourism destinations, where individuals with strong place attachment generally exhibit more proactive pro-environmental behavior patterns [24]. Ju et al., using fuzzy-set qualitative comparative analysis, found that social embeddedness and place attachment are necessary conditions for heritage responsibility behavior among tourism community residents; however, the internal mechanism of this relationship remains to be elucidated [70]. Based on the above analysis, this study hypothesizes that social embeddedness influences residents’ emotional connection, identification, and dependence on the place through multiple dimensions, promoting more pro-environmental behavior, which is theoretically reasonable. Therefore, the following hypotheses are proposed:

Hypothesis 6 (H6).

PA has a significant positive effect on PEB.

Hypothesis 7 (H7).

PA mediates the relationship between COE, RE, SE, IE, CUE and PEB.

Tourism systematically drives the formation of residents’ place attachment by strengthening economic dependence through job creation and enhancing cultural identification to increase a sense of belonging. Empirical research shows a significant positive correlation between the degree of tourism economic dependence and the strength of place identity. Tang et al., using free and semi-structured interviews with residents of Guling Community in Lushan during the construction of the national park, found that local residents’ dependence on the tourism economy is an important factor in place attachment and that identification with tourism culture fosters place identity [71]. Xu et al. divided perceptions of tourism impacts into perceived benefits and perceived costs, and their structural equation modeling revealed that residents’ perceived benefits of tourism enhance their place attachment [72].

Based on the S-O-R theoretical framework, social embeddedness serves as an external stimulus source that ultimately affects pro-environmental behavioral practices through the dual mediating paths of perceptions of tourism impacts and place attachment. Based on this analysis, the following hypotheses are proposed:

Hypothesis 8 (H8).

PTIs have a significant positive effect on PA.

Hypothesis 9 (H9).

PTI and PA jointly play a chained mediating role in the influence of COE, RE, SE, IE, CUE on PEB.

In summary, based on H1–H9, this study constructs a chained mediation model with perceptions of tourism impacts and place attachment as mediating variables to explain the mechanism by which cognitive, structural, relational, institutional, and cultural embeddedness affects residents’ pro-environmental behavior and to further verify the relationship between place attachment and perceptions of tourism impacts (Figure 1).

4. Research Design

4.1. Study Area

The Giant Panda National Park commands paramount international attention due to its flagship species, exceptional biodiversity, and complex geological landscape. Its protected area spans 12 cities across Sichuan, Shaanxi, and Gansu provinces, covering a total area of 27,134 km². Over 120,000 people reside within the park boundaries [12]. As a critical area for biodiversity conservation, the park attracts significant interest from conservation enthusiasts, researchers, and tourists due to its unique natural landscapes, rich biodiversity (notably the umbrella species giant panda and over 10,000 associated rare species like the golden snub-nosed monkey, snow leopard, and Chinese yew), and conservation management model. Public support for giant panda conservation is high, with strong interest in the park’s protection measures and ecotourism activities [73].

Baoxing County, located in Ya’an City, Sichuan Province, is the scientific type locality of the world’s first giant panda (Ailuropoda melanoleuca), discovered and named by French missionary Armand David in 1869. As a core area of the park (Figure 2), 81.7% of the county’s territory is incorporated into the park, supporting an average density of one wild giant panda per 17 km² [74].

Baoxing represents not only the global center of giant panda scientific discovery and conservation practice but also demonstrates three key innovations: an integrated biodiversity network characterized by its uniquely formed conservation structure; sustainable economic restructuring evidenced by a successful transition from traditional logging and mining to ecotourism (featuring 6 national 4A-level scenic areas) and green agriculture; and a community governance system utilizing Giant Panda National Park ecological compensation policies and community covenants to incentivize green industry transformation while defining resource use boundaries, combined with an innovative resident co-management mechanism to activate endogenous community conservation motivation [75].

Therefore, as the core conservation frontier of the GPNP, Baoxing County is an ideal case study location. The livelihood activities and environmental behaviors of its residents directly impact the habitat security of giant pandas and other rare species, occurring under intense public scrutiny and policy regulation, making it ideal for researching the formation mechanisms of pro-environmental behavior and its interaction with social and policy environments.

4.2. Questionnaire Design and Variable Measurement

Data for this study were collected through on-site random distribution of questionnaires. The questionnaire consists of two parts: the first part includes 38 items measuring four latent variables, namely residents’ social embeddedness, perceptions of tourism impacts, place attachment, and pro-environmental behaviors. All measurement items are presented in Appendix A. To ensure the reliability and validity of the questionnaire, the dimensions and items of the scales were derived from previous studies, with adjustments made to align with the actual conditions of the case site. Statistical validation was conducted using structural equation modeling (SEM). A 5-point Likert scale was adopted for all items, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The specific measurement methods are as follows:

(1)

Social Embeddedness: For relational embeddedness, structural embeddedness, and institutional embeddedness, this study referred to the social embeddedness scale developed by Wang et al. [35] in their research on residents’ pro-tourism behavior. Cognitive embeddedness items were designed based on the scale used by Tan et al. [33] for measuring farmers’ pro-environmental behavior, including the question “I know how to respond if I encounter wild animals or plants.” For material culture in cultural embeddedness, items were adapted from Dean’s scale [40] measuring residents’ water-saving behavior; for behavioral culture, items referred to the scale used by Zhang et al. [41] on farmers’ farmland transfer behavior, including the item “In this area, protecting the environment of the Giant Panda National Park is respected by people.” A total of 20 items were designed.

(2)

Place Attachment: Four items were adopted from the scale developed by Williams et al. [38], which has been applied in empirical studies on community residents by Professor Zhang [36] and on rural tourism residents by Professor Jia [76].

(3)

Perceptions of Tourism Impacts: This part used the scale designed by Professor Lu [77] for measuring residents’ perceptions of tourism impacts, combined with the scale developed by Hu et al. [78] for ecotourism development among residents in Xiaoxiangling and its surrounding areas in the Giant Panda National Park, resulting in eight items such as “The development of the Giant Panda National Park has promoted the local economy.”

(4)

Pro-Environmental Behavior: Six items were adapted from the scales used by Zheng et al. [79] for measuring urban residents’ pro-environmental behavior and by Cao et al. [25] for residents in tourism destinations, including the item “I will comply with regulations and avoid causing damage to the environment of the Giant Panda National Park.” Ultimately, a questionnaire with 38 items was developed.

The second part of the questionnaire collected respondents’ demographic information, including age, gender, occupation, and education level. Additionally, it included items related to pro-environmental behavior such as political affiliation, length of residence, involvement in the tourism industry, whether family income depends on tourism, and awareness that four-fifths of Baoxing County’s area falls within the ecological red line.

5. Results and Analysis

5.1. Descriptive Statistics of the Sample

Before officially distributing the questionnaire, a preliminary survey was conducted on 20 September 2024, in Baoxing County, Ya’an City. A total of 60 questionnaires were distributed, and 47 were recovered. After testing and eliminating items with abnormal factor loadings, eight variables showed good reliability and validity. Subsequently, from 5 to 10 October and 28 to 30 December 2024, the research team carried out the formal survey in Baoxing County, focusing on the county town, Dengchigou Panda New Village, and Xueshan Village, using random sampling.

To improve the reliability of the questionnaire data, given that many respondents had limited education, necessary explanations were provided to respondents with reading or comprehension difficulties to ensure they fully understood the questions before completing the questionnaire. A total of 400 questionnaires were distributed during the formal survey, and 379 were recovered. After excluding invalid responses, 326 valid questionnaires were obtained, with a valid response rate of 86.01% (

Table 2. Characteristics of Respondents.

Item	Category	Percentage	Item	Category	Percentage
Gender	Male	56.4%	Length of Residence	≤5 years	2.1%
	Female	43.6%		6–10 years	6.7%
Age	<18	5.2%		>10 years	91.1%
	19–25	16.6%	Occupation	Government/Institution Staff	7.7%
	26–30	14.4%		Company Employee	9.5%
	31–40	18.1%		Professional	8.9%
	41–50	24.5%		Freelancer/Self-employed	37.1%
	51–60	6.4%		Student	8.3%
	>61	14.7%		Farmer	14.1%
Education Level	Junior High School or below	44.5%		Unemployed/Retired	14.4%
	High School or Vocational College	24.2%	Monthly Income Level	<2000 RMB	33.4%
	Bachelor’s or Associate Degree	29.4%		2001–5000 RMB	29.1%
	Master’s Degree or above	1.8%		5001–8000 RMB	35.3%
Personal Involvement in Tourism	Yes	13.5%		8001–10,000 RMB	0.09%
	No	86.5%		≥10,001 RMB	1.2%
Family Involvement in Tourism	Yes	22.7%
	No	77.3%

). Overall, the structure of the survey sample is reasonable, scientifically sound, and representative, making it suitable for subsequent analysis.

5.2. Common Method Bias Test

This study implemented diverse strategies across procedural control and statistical control dimensions to mitigate the impact of common method bias (CMV), using SPSS 26.0. In terms of procedural control, respondents were assured that questionnaire data would only be used for academic research, strict anonymity was guaranteed, and all professional terms were explained to enhance data objectivity and reliability. For statistical control, Harman’s single-factor test method was used. Exploratory factor analysis without rotation was conducted on all items. The results showed 8 factors with eigenvalues >1, with the first factor explaining 36.048% of the variance, below the critical threshold of 40%, indicating no significant common method bias in this study.

5.3. Reliability, Validity Tests, and Confirmatory Factor Analysis

This study tested the reliability and validity of the questionnaire using SPSS 26.0. Principal component analysis was applied, extracting factors with eigenvalues greater than 1. A total of eight factors were extracted, with a cumulative variance contribution rate of 68.625%. After orthogonal rotation, the 38 items were categorized into eight factors, and each research item showed a strong association with its respective factor, indicating good structural validity of the scale. The overall Cronbach’s alpha of the scale was 0.95, and the Cronbach’s alpha values for each of the eight latent variables were all above 0.8, demonstrating high reliability of the questionnaire.

Subsequently, confirmatory factor analysis (CFA) was performed using the maximum likelihood estimation method in Amos 26.0. All item factor loadings were greater than 0.6 (Figure 3). Regarding discriminant validity (AVE), the square root of the AVE for each latent variable in this study was larger than the correlation coefficients between it and any other latent variable, indicating good discriminant validity. In terms of composite reliability (CR), all latent variables had CR values above 0.8 (

Table 3. Results of Confirmatory Factor Analysis.

Latent Construct	Indicator	Std	Cronbach’s α	CR	AVE
Reference Value		>0.5	>0.8	>0.7	>0.5
COE	COE1	0.713	0.862	0.866	0.621
	COE2	0.696
	COE3	0.883
	COE4	0.843
RE	RE1	0.81	0.858	0.859	0.603
	RE2	0.77
	RE3	0.75
	RE4	0.776
Latent Construct	Indicator	Std	Cronbach’s α	CR	AVE
Reference Value		>0.5	>0.8	>0.7	>0.5
SE	SE1	0.786	0.856	0.856	0.598
	SE2	0.762
	SE3	0.754
	SE4	0.791
IE	IE1	0.755	0.866	0.867	0.620
	IE2	0.836
	IE3	0.794
	IE4	0.761
CUE	CUE1	0.773	0.857	0.858	0.602
	CUE2	0.756
	CUE3	0.825
	CUE4	0.747
PTI	PTI1	0.755	0.921	0.921	0.594
	PTI2	0.801
	PTI3	0.802
	PTI4	0.798
	PTI5	0.783
	PTI6	0.771
	PTI7	0.705
	PTI8	0.748
PA	PA1	0.742	0.837	0.839	0.566
	PA2	0.815
	PA3	0.726
	PA4	0.722
PEB	PEB1	0.78	0.877	0.878	0.548
	PEB2	0.717
	PEB3	0.783
	PEB4	0.794
	PEB5	0.727
	PEB6	0.626

), reaching ideal levels and indicating high internal consistency and good convergent validity of the latent variables.

5.4. Correlation Analysis

Pearson correlation analysis was conducted to examine the relationships among the eight variables: cognitive embeddedness, relational embeddedness, structural embeddedness, institutional embeddedness, cultural embeddedness, place attachment, perceptions of tourism impacts, and pro-environmental behavior. As shown in

Table 4. Correlation Analysis Results of Variables (N = 326).

	1	2	3	4	5	6	7	8
Cognitive Embeddedness	1
Relational Embeddedness	0.351 **	1
Structural Embeddedness	0.434 **	0.356 **	1
Institutional Embeddedness	0.352 **	0.297 **	0.250 **	1
Cultural Embeddedness	0.349 **	0.383 **	0.397 **	0.209 **	1
Place Attachment	0.483 **	0.450 **	0.489 **	0.359 **	0.499 **	1
Perceptions of Tourism Impacts	0.483 **	0.447 **	0.482 **	0.381 **	0.452 **	0.543 **	1
Pro-Environmental Behavior	0.539 **	0.512 **	0.531 **	0.408 **	0.517 **	0.627 **	0.602 **	1

Note: ** indicates that p is significant at the 0.01 significance level.

, the correlations between each pair of variables were all greater than 0, indicating positive relationships among all variables.

5.5. Analysis of Model Fit

Table 5. Goodness-of-Fit Indices of the Structural Equation Model.

Index	CMIN	DF	CMIN/DF	GFI	RMSEA	CFI	NFI	IFI
Ideal Value	-	-	<3	>0.9	<0.08	>0.9	>0.9	>0.9
Threshold	-	-	<5	>0.8	<0.10	>0.8	>0.8	>0.8
Model Value	853.854	637	1.34	0.882	0.032	0.969	0.888	0.969

details the key fit indices obtained from testing the structural model. By comparing the model’s values with recommended thresholds, it can be concluded that the constructed model demonstrates good overall fit.

The table presents the model’s fit indices: the CMIN value is 853.854 with DF = 637, resulting in a CMIN/DF of 1.34, which is below the recommended threshold of 3 and therefore ideal. RMSEA is 0.032 (<0.08), while CFI and IFI are both above 0.9. GFI and NFI exceed 0.8. Collectively, these indicators demonstrate that the model has a good overall fit.

5.6. Hypothesis Testing

The standardized path coefficients in the structural equation model reflect the significance of relationships among variables.

Table 6. Summary of Model Coefficients.

Hypothesis Path	Standardized Path Coefficient	S.E.	C.R.	p	Test Result
H1a: CE → PEB	0.143	0.067	2.618	0.009	Support
H1b: RE → PEB	0.151	0.054	2.819	0.005	Support
H1c: SE → PEB	0.148	0.064	2.574	0.01	Support
H1d: IE → PEB	0.11	0.054	2.259	0.024	Support
H1e: CE → PEB	0.145	0.06	2.582	0.01	Support
H2a: CE → PTI	0.183	0.065	3.015	0.003	Support
H2b: RE → PTI	0.183	0.053	3.065	0.002	Support
H2c: SE → PTI	0.234	0.062	3.708	***	Support
H2d: IE → PTI	0.172	0.054	3.124	0.002	Support
H2e: CE → PTI	0.211	0.057	3.478	***	Support
H3: PTI → PEB	0.152	0.069	2.503	0.012	Support
H5a: CE → PA	0.173	0.07	2.779	0.005	Support
H5b: RE → PA	0.153	0.056	2.494	0.013	Support
H5c: SE → PA	0.191	0.067	2.923	0.003	Support
H5d: IE → PA	0.113	0.057	2.014	0.044	Support
H5e: CE → PA	0.226	0.062	3.567	***	Support
H6: PA → PEB	0.268	0.081	3.639	***	Support
H8: PTI → PA	0.196	0.072	2.713	0.007	Support

Note: *** indicates that p is significant at the 0.001 significance level.

presents the relationships between latent variables in the structural model along with other test parameters. All standardized path coefficients were greater than 0.1, indicating significant positive effects between variables. These results support hypotheses H1 (a–e), H2 (a–e), H3, H5 (a–e), H6, and H8.

5.7. Mediation Effect Test

Using the bootstrap method, the mediating effects of perceptions of tourism impacts and place attachment were tested. For hypotheses H4, H7, and H9, a 95% confidence level was set, and 5000 resamples were drawn to analyze the individual mediation of perceptions of tourism impacts and place attachment, as well as their chained mediation, sequentially. As shown in

Table 7. Standardized Direct, Indirect, and Total Mediation Effects of Hypothetical Model.

Path Relationship	Direct Effect	Indirect Effect	95% Confidence Interval		p	Conclusion
			Lower	Upper
CE → PTI → PA → PEB	0.175(0.009)	0.011	0.001	0.046	0.021	Support
CE → PTI → PEB	0.175(0.009)	0.034	0.005	0.097	0.02	Support
CE → PA → PEB	0.175(0.009)	0.057	0.008	0.149	0.017	Support
RE → PTI → PlA → PEB	0.152(0.005)	0.009	0.001	0.032	0.019	Support
RE → PTI → PEB	0.152(0.005)	0.028	0.002	0.084	0.032	Support
RE → PA → PEB	0.152(0.005)	0.041	0.006	0.111	0.023	Support
SE → PTI → PA → PEB	0.166(0.01)	0.013	0.002	0.044	0.014	Support
SE → PTI → PEB	0.166(0.01)	0.04	0.006	0.102	0.017	Support
SE → PA → PEB	0.166(0.01)	0.058	0.011	0.144	0.004	Support
IE → PTI → PA → PEB	0.123(0.024)	0.01	0.001	0.038	0.016	Support
IE → PTI → PEB	0.123(0.024)	0.029	0.004	0.082	0.018	Support
IE → PA→ PEB	0.123(0.024)	0.034	0.001	0.099	0.043	Support
CE → PTI→ PA → PEB	0.156(0.01)	0.012	0.001	0.044	0.019	Support
CE → PTI → PEB	0.156(0.01)	0.035	0.005	0.098	0.017	Support
CE→ PA → PEB	0.156(0.01)	0.065	0.017	0.165	0.002	Support

, the bias-corrected 95% confidence intervals did not include zero, indicating that the indirect effects of each dimension of social embeddedness on residents’ pro-environmental behavior in Baoxing County, Giant Panda National Park, through perceptions of tourism impacts were significant, thus supporting hypothesis H4. The indirect effects through place attachment were also significant, supporting hypothesis H7. Moreover, the chained mediation path “social embeddedness dimensions → perceptions of tourism impacts → place attachment → pro-environmental behavior” had a significant positive predictive effect, confirming hypothesis H9.

5.8. Multiple Linear Regression Analysis

Using pro-environmental behavior as the dependent variable, cognitive embeddedness (COE), relational embeddedness (RE), structural embeddedness (SE), institutional embeddedness (IE), cultural embeddedness (CUE), perceptions of tourism impacts (TI), and place attachment (PA) as independent variables, and introducing demographic variables as control variables, hierarchical regression was conducted. Specific assignments are shown in the variable assignment table (

Table 8. Variable Coding Table.

Category	Assignment Method
Gender	1 = Male; 2 = Female
Age	1 = Under 18 years old; 2 = 18–25 years old; 3 = 26–30 years old; 4 = 31–40 years old; 5 = 41–50 years old; 6 = 51–60 years old; 7 = Over 60 years old
Residence Duration	1 = 1–5 years; 2 = 6–10 years; 3 = Over 10 years
Educational Background	1 = Junior high school or below; 2 = High school or vocational school; 3 = Undergraduate or vocational school; 4 = Graduate or above
Participate in tourism industry	1 = Yes; 2 = No
Income	1 = Below 2000 yuan; 2 = 2001–5000 yuan; 3 = 5001–8000 yuan; 4 = 8001–10,000 yuan; 5 = 10,001 yuan and above;
Cognitive Embeddedness	Measured Value Input
Relational Embeddedness	Measured Value Input
Structural Embeddedness	Measured Value Input
Institutional Embeddedness	Measured Value Input
Cultural Embeddedness	Measured Value Input
Place Attachment	Measured Value Input
Perception of Tourism Impacts	Measured Value Input
Pro-environmental behavior	Measured Value Input

). Model 2, containing the main variables, represents the regression analysis of COE, RE, SE, IE, CUE, PA, and TI on PEB under the control of the control variables (

Table 9. Results of Linear Regression Analysis.

Variable	Dependent Variable: Pro-environmental Behavior
	Model 1			Model 2
	B	Standard Error	t	B	Standard Error	t
(Constant)	3.789 **	0.488	7.770	0.339	0.365	0.928
Gender	0.028	0.090	0.312	0.003	0.058	0.054
Age	−0.015	0.025	−0.612	−0.007	0.016	−0.447
Educational Background	0.045	0.050	0.908	0.041	0.032	1.278
Participate in tourism industry	0.064	0.131	0.493	−0.052	0.086	−0.606
Income	0.041	0.049	0.847	0.037	0.031	1.181
Residence Duration	−0.124	0.118	−1.051	−0.202 **	0.077	−2.611
Cognitive Embeddedness				0.157 **	0.047	3.326
Relational Embeddedness				0.153 **	0.043	3.598
Structural Embeddedness				0.167 **	0.048	3.483
Institutional Embeddedness				0.109 **	0.040	2.722
Cultural Embeddedness				0.169 **	0.045	3.773
Place Attachment				0.198 **	0.051	3.915
Perception of Tourism Impacts				0.166 **	0.051	3.252
R2	0.012			0.602
Adjusted R2	−0.007			0.585
F	F = 0.621 p = 0.713			F = 36.312 p = 0.000

Note: ** indicates that p is significant at the 0.01 significance level.

). The F-value was 36.312, with a significance p-value of 0.000, indicating significance at the 0.05 level, and the model is valid. The R² value of the model was 0.602, meaning the independent variables can explain 60.2% of the variation in PEB. Under the control of the control variables, the regression coefficient for COE was 0.157 (t = 3.326, p = 0.001 < 0.01), indicating COE has a significant positive effect on PEB. The regression coefficient for RE was 0.153 (t = 3.598, p = 0.000 < 0.01), indicating RE has a significant positive effect on PEB. The regression coefficient for SE was 0.167 (t = 3.483, p = 0.001 < 0.01), indicating SE has a significant positive effect on PEB. The regression coefficient for IE was 0.109 (t = 2.722, p = 0.007 < 0.01), indicating IE has a significant positive effect on PEB. The regression coefficient for CUE was 0.169 (t = 3.773, p = 0.000 < 0.01), indicating CUE has a significant positive effect on PEB. The regression coefficient for PA was 0.198 (t = 3.915, p = 0.000 < 0.01), indicating PA has a significant positive effect on PEB. The regression coefficient for TI was 0.166 (t = 3.252, p = 0.001 < 0.01), indicating TI has a significant positive effect on PEB.

6. Discussion

Based on the “Stimulus-Organism-Response” research approach, a dual-mediating chain model of social embeddedness influencing pro-environmental behavior was constructed, and by analyzing its formation pathway, the driving factors of pro-environmental behavior among residents in the Giant Panda National Park were explained.

(1) The social embeddedness mechanisms in the Giant Panda National Park community significantly and positively influence residents’ pro-environmental behavior through the synergistic effect of five dimensions: cognitive, relational, structural, institutional, and cultural. Specifically, relational embeddedness (β = 0.151) > structural embeddedness (β = 0.148) > cultural embeddedness (β = 0.145) > cognitive embeddedness (β = 0.143) > institutional embeddedness (β = 0.11). The essence of a community is a network of relationships, and residents’ pro-environmental behavior is influenced by others [80]. Communities surrounding the national park are typically traditional, geographically or kinship-based villages with close ties, where interpersonal relationships are the core of daily life. Structural embeddedness reflects residents’ positions in the community social network, such as being at the center of the network or having more connections (e.g., village cadres, local elites, information hub figures), whose behavior has greater influence and is more likely to access information and resources to support pro-environmental behavior. Network structure provides channels for the dissemination of information and behavior. In the cultural embeddedness aspect, differing from previous scholars who adopted a macro perspective on sociocultural values [79], this study focuses on the micro level of community culture. Community cultural embeddedness, through the internalization of community environmental culture, integrates environmentally friendly values and behavioral norms into residents’ daily lives, encouraging residents to view environmental protection as part of their personal identity. Cognitive embeddedness enhances residents’ awareness of environmental issues, stimulating their intrinsic motivation and driving pro-environmental behavior. This result once again demonstrates the profound impact of cognitive processes on behavioral decision-making in cognitive psychology, particularly by increasing individuals’ awareness of specific issues, which can effectively stimulate their intrinsic motivation and encourage them to take corresponding actions [81], Institutional embeddedness constrains and guides residents’ behavior through policy norms, aligning their actions with social expectations. Compared to the non-significant results in Zhang Yuqin’s [82] and other studies, which only discussed the non-significant impact of government incentives on farmers’ ecological farming behavior, the mechanism of institutional embeddedness can be supplemented by explaining its role from two dimensions: policy systems and behavioral norms. A potential reason for the relatively low institutional embeddedness lies in the fact that formal institutions are generally perceived as externally imposed rules rather than endogenously formed community consensus. Residents may comply out of a desire to avoid punishment rather than from intrinsic identification, resulting in low autonomy and durability of their behavioral motivations. Furthermore, ordinary residents may not fully understand or endorse the specific provisions, which undermines the effectiveness of such institutions in guiding behavior.

(2) Perceptions of tourism impacts, as one of the mediating variables, played a key role in the influence of social embeddedness on residents’ pro-environmental behavior in the Giant Panda National Park community. Social embeddedness shapes residents’ positive perceptions of tourism’s economic and ecological benefits through four pathways: educational communication, information sharing, policy guidance, and cultural immersion. For example, residents widely recognize that ecotourism contributes to increased income and species conservation. This positive perception encourages residents to weigh the costs and benefits of pro-environmental behavior and to actively participate in ecological protection projects or limit resource consumption. As a typical example of China’s nature reserve system, residents in the Giant Panda National Park understand that a good natural environment and scenic beauty are key attractions for tourists. The more residents perceive positive tourism impacts, the more they benefit from pro-environmental behaviors and the more likely they are to practice them. This finding aligns with the views of Liu [57], Cheng [23], and Li [63], revealing a dual driving mechanism of economic rationality and ecological responsibility.

(3) Social embeddedness indirectly drives pro-environmental behavior by strengthening place attachment. Empirical studies have shown that residents’ place attachment significantly affects their pro-environmental resource management behavior in various contexts [26,64,65]. This study further demonstrates that within the Giant Panda National Park, cognitive, structural, relational, institutional, and cultural embeddedness play important roles in enhancing residents’ emotional bonds with their community. Cognitive embeddedness deepens ecological responsibility awareness, as residents participate in ecological education programs in the park, understanding its uniqueness as a natural heritage site and developing a “protection as mission” mindset. Relational and structural embeddedness foster community cohesion; frequent neighborly interactions and collaborative patrolling activities strengthen collective belonging, shaping a “homeland community” identity. Institutional embeddedness enhances environmental governance effectiveness, as ecological compensation policies and community conventions not only improve environmental quality but also increase residents’ trust in institutional fairness, motivating them to proactively uphold ecological rules. Cultural embeddedness integrates tradition and modernity, with community material and behavioral cultures shaping a “guardian” identity. Among the embedded dimensions, cultural embeddedness has the highest impact coefficient on place attachment (β = 0.226), indicating that cultural atmosphere and value norms are more likely to trigger residents’ emotional identification with their homeland. In contrast, the path coefficients of institutional embeddedness on place attachment and pro-environmental behavior are relatively low, indicating that external norms have limited behavioral incentive effects in the absence of trust or emotional bonds.

(4) The study reveals that social embeddedness drives pro-environmental behavior through a chained mediation path of “perceptions of tourism impacts → place attachment,” forming a transmission mechanism of “social embeddedness → perceptions of tourism impacts → place attachment → pro-environmental behavior.” The underlying reason is that Baoxing County, as the birthplace of the giant panda, has implemented environmental protection measures that deeply influence local residents’ behavior through various forms of social embeddedness, which collectively enhance residents’ perceptions of tourism impacts. Residents increasingly recognize the positive role of ecotourism in environmental protection. As tourism in the area continues to expand, it creates numerous employment opportunities, making more local residents’ livelihoods dependent on the Giant Panda National Park’s tourism industry. In other words, the growth of tourism strengthens residents’ dependence on the place. Meanwhile, as the park’s reputation increases and the number of visitors rises each year, residents’ pride and identification with the Giant Panda National Park are greatly enhanced. This strengthened place attachment significantly improves residents’ awareness of nature conservation, further reinforcing their intentions to engage in pro-environmental behavior. Similar mediation paths have been confirmed in other studies, such as Cai et al., who found a path of “perceived tourism benefits → place dependence → cultural preservation attitudes and behavior” among residents of the Xijiang Miao Village community [37].

7. Conclusions and Implications

7.1. Research Conclusions

This study focuses on the pro-environmental behaviors of community residents in the Giant Panda National Park. Using structural equation modeling (SEM), it empirically analyzes the influence pathways of the five dimensions of social embeddedness (cognitive, relational, structural, institutional, and cultural) on perceptions of tourism impacts, place attachment, and pro-environmental behaviors.

The multiple dimensions of social embeddedness (cognitive, relational, structural, institutional, and cultural) synergistically drive environmental protection practices through both direct effects and indirect pathways. Among them, relational embeddedness and structural embeddedness exhibit the most significant direct impacts, highlighting the core mobilizing role of community interpersonal networks and individuals’ social positions in promoting environmental actions. Perceptions of tourism impacts (PTIs) and place attachment (PA) serve as key mediators—social embeddedness not only directly stimulates behaviors but also indirectly facilitates behavioral transformation by enhancing residents’ awareness of the ecological benefits of tourism and deepening their emotional attachment. Notably, the study identifies a chain transmission mechanism of “social embeddedness →PTI → PA → PEB,” revealing that residents, within multiple social bonds, strengthen their sense of home identity due to positive perceptions of tourism development, thereby translating emotional attachment into proactive conservation actions. It is worth noting that the effects of different embeddedness dimensions exhibit heterogeneity: cultural embeddedness exerts the strongest shaping influence on place attachment, indicating that the community cultural atmosphere is the core crucible for forging emotional bonds; structural embeddedness predominantly influences the formation of tourism impact perceptions, validating the role of social networks in transmitting ecological awareness.

Finally, the temporal and spatial limitations of this study should be acknowledged. Based on cross-sectional data from a single time point in Baoxing County, it is difficult to capture the dynamic process of behavioral evolution. Future research should conduct cross-regional comparative studies across more national parks. Additionally, although this study conceptualizes social embeddedness as a multidimensional structure, it may have omitted other relevant dimensions, which can be explored in future research. Furthermore, the research site is limited to a single region (Baoxing County), restricting the generalizability of the findings. Therefore, it is necessary to elaborate on potential differences in these findings within the context of other national parks.

7.2. Managerial Implications

Using Baoxing County as a case, this study explores the relationship between social embeddedness and residents’ pro-environmental behavior, providing practical insights for the management and development of resident environmental protection in the Giant Panda National Park.

(1) The study highlights the importance of social networks. National park management authorities should emphasize community participation and cooperation by organizing a variety of environmental protection and community cultural activities, creating platforms for resident interaction to continuously stimulate their environmental awareness and innovative vitality.

(2) The research confirms that when residents perceive positive impacts of tourism development, their pro-environmental behavior is further encouraged. Therefore, tourism management departments should aim to improve residents’ living standards, optimize tourism benefit distribution mechanisms, and help residents benefit from tourism development, thereby enhancing their confidence in ecotourism.

The study demonstrates the positive influence of the community’s cultural environment on residents’ pro-environmental behavior and place attachment. Communities should balance ecological conservation with community development, creating a healthy and progressive community cultural environment. High-quality cultural offerings can strengthen residents’ sense of environmental responsibility and belonging.